Arma model order estimation using third order computations

A novel approach for estimating the order of a non-Gaussian autoregressive moving-average process is proposed. The proposed method uses third order computations, and is based on the minimum eigenvalue of a family of covariance matrices derived from the observed output data. The algorithm uses data matrices rather than calculated cumulants. Hence, we avoid the non-stationary effects, which is due to finite-length observations. Examples are given to demonstrate the performance of the proposed algorithm.